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Volume 6 Issue 1

Privacy Preserving Distributed DBSCAN Clustering

Jinfei Liu^(a),(*), Li Xiong^(a), Jun Luo^(b), Joshua Zhexue Huang^(b)

Transactions on Data Privacy 6:1 (2013) 69 - 85

Abstract, PDF

(a) Department of Mathematics & Computer Science, Emory University, 30322, USA.

(b) Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, 518055, China.

e-mail:jinfei.liu @emory.edu; lxiong @mathcs.emory.edu; jun.luo @siat.ac.cn; zx.huang @siat.ac.cn

Abstract

DBSCAN is a well-known density-based clustering algorithm which offers advantages for finding clusters of arbitrary shapes compared to partitioning and hierarchical clustering methods. However, there are few papers studying the DBSCAN algorithm under the privacy preserving distributed data mining model, in which the data is distributed between two or more parties, and the parties cooperate to obtain the clustering results without revealing the data at the individual parties. In this paper, we address the problem of two-party privacy preserving DBSCAN clustering. We first propose two protocols for privacy preserving DBSCAN clustering over horizontally and vertically partitioned data respectively and then extend them to arbitrarily partitioned data. We also provide performance analysis and privacy proof of our solution..

^* Corresponding author.